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Pharmacometrics Under Pressure

開催日: 2026年4月30日 – 10:00 - 11:00am ET
2026年5月6日 – 10:00 - 11:00am ET
2026年5月15日 – 10:00 - 11:00am ET

開催時間: 10:00 - 11:00am ET

関連サービス：Pharmacometrics Services, Clinical Pharmacology and Translational Medicine

関連ソフトウェア： Phoenix® NLME, R Speaks NLME (RsNLME)

An NLME workflow stress test

In drug development, it’s not the first model run that matters. It’s what happens when the questions keep coming, timelines compress, and the model evolves beyond the original plan. In this three-part webinar series, we put one NLME toolset through an endurance test designed to mirror real programs: rapid clinical pharmacology decisions, project-scale population modeling, and expert-level PK/PD complexity. Along the way, you’ll learn whether solutions from Certara Phoenix can meet your needs and rival similar toolsets for precision, speed, and flexibility, all without needing to switch tools when things get harder.

学べる内容：

  • Understand the power and flexibility of R in conjunction with the Phoenix NLME engine
  • Explore how GUI and code-based environments support one another through complex workflows
  • Benchmark your model convergence speeds against those enabled by automatic-differentiation-assisted parametric optimization

This webinar series is ideal for:

  • Quantitative Clinical Pharmacologists
  • Translational Pharmacologists
  • PK/PD Modelers
  • Quantitative Clinical Pharmacologists
  • Early career pharmacometricians
  • Experienced pharmacometricians

お申込みはこちら


Part 1 – The Quantitative Clin Pharm Sprint

Thursday, 30 April 2026
10am ET

Every endurance effort begins with a critical first stretch: you need speed and stability. This session focuses on how Phoenix NLME’s and RsNLME’s inbuilt flexibility supports quantitative clinical pharmacology workflows where decisions must be made early, clearly, and defensibly.

In this session, we’ll first explore the importance of a toolset that adapts to the pharmacometrician’s needs, as determined by her skill set, interface preference, and use case. Next, we’ll introduce an unconditional demand: a fast, accurate, and robust engine for model fitting. Finally, we’ll explore two use cases—one translational, and one exposure-response—where tool switching and data updates can sometimes pose hurdles to efficiency.

Introduction: “Meeting Modelers When the They Are”
Keith Nieforth, PharmD, Senior Director, Pharmacometrics Software, Certara

Use cases covered:

Phoenix NLME for First-in-Human Dose Selection
Elliot Offman, BSc Pharm, MSc, PhD, Vice President, Clinical Pharmacology & Translational Medicine, Certara Drug Development Solutions

RsNLME for Adverse Event Probability: Logistic Regression on Exposure-Response Data
Mahmoud Ali, MsC, Associate Scientist, Certara Drug Development Solutions

Ideal for:

  • Quantitative Clinical Pharmacologists
  • Translational Pharmacologists
  • PK/PD Modelers

Part 2 – The Steady Miles: Project-Grade PMx Without Losing Pace

Wednesday, 6 May 2026
10am ET

This is where most teams feel the strain: multiple analyses, stakeholder revisions, and “can we deliver by Friday?” constraints. In this webinar, we show how Phoenix NLME and/or RsNLME support standard population modeling workflows in a way that stays maintainable as the project expands. The theme is continuity—keeping pace while requirements change—so you can adopt NLME without fearing a slowdown or a loss of deliverable quality.

Use cases covered:

TMDD（Target-Mediated Drug Disposition）モデリング
Kathleen Koeck, PhD, Director, Certara Drug Development Solutions

Compartmental popPK for a CNS Therapy
Nathalie Gosselin, PhD, Vice President, Certara Drug Development Solutions

Ideal for:

  • Quantitative Clinical Pharmacologists
  • Early career pharmacometricians

Part 3 – The Wall: When the Model Gets Harder Than Planned

Wednesday, 15 May 2026
10am ET

In endurance testing, “the wall” is where systems either adapt—or fail. In modeling, it’s the moment the dataset grows, the mechanism asserts itself, or the model needs to become more sophisticated than originally scoped. This final session demonstrates an expert workflow with Phoenix/RsNLME that can absorb complexity without forcing a tool change. We’ll walk through a full population PK/PD example that reflects the kind of real-world modeling depth that often triggers a switch to another platform.

Use case covered:

Simultaneous PKPD Model for Concentration vs Parasite Load in Malaria
Wynand Smythe, BPharm, MSc, PhD, Associate Director, Certara Drug Development Solutions
&
Amira Ghoneim, PhD, Associate Scientist, Certara Drug Development Solutions

Modeling G-CSF Effects in RsNLME
Stephen Duffull, MPharm, PhD, Senior Scientific Advisor, Quantitative Science Services, Certara

Ideal for:

  • Experienced pharmacometricians

Key learning objectives for the series:

  • Understand the power and flexibility of R in conjunction with the Phoenix NLME engine
  • Explore how GUI and code-based environments support one another through complex workflows
  • Benchmark your model convergence speeds against those enabled by automatic-differentiation-assisted parametric optimization

講演者

Amira Ghoneim, PhD

Associate Scientist, Certara Drug Development Solutions

Elliot Offman, BSc Pharm, MSc, PhD

Vice President, Clinical Pharmacology & Translational Medicine, Certara Drug Development Solutions

Kathleen Koeck, PhD

Director, Certara Drug Development Solutions

Keith Nieforth, PharmD

Senior Director, Pharmacometrics Software, Certara

Nathalie Gosselin, PhD

サターラ医薬品開発ソリューション担当副社長

Stephen Duffull, MPharm, PhD

Senior Scientific Advisor, Quantitative Science Services, Certara

Wynand Smythe, BPharm, MSc, PhD

Associate Director, Certara Drug Development Solutions

Mahmoud Ali, MsC

Associate Scientist, Certara Drug Development Solutions

“"After 30 years of using other platforms, the biggest change I experienced when moving analyses to RsNLME (other than the streamlined workflow) have been the significant improvements in speed and stability. The many, additional runs and tweaks that were often needed to achieve a successful run are just not needed with RsNLME. It just seems to work. I now spend less time worrying about runs and more time on the models."”

Stephen DuffullSenior Scientific Advisor, Quantitative Science Services at Certara

お申込みはこちら


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